Nowcasting economic activity in Argentina with many predictors
- Autores
- D'Amato, Laura; Garegnani, María Lorena; Blanco, Emilio
- Año de publicación
- 2011
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- We pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future.
Facultad de Ciencias Económicas - Materia
-
Ciencias Económicas
Forecast pooling
Large dataset
Real time forecast
Factor Models - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/170412
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Nowcasting economic activity in Argentina with many predictorsD'Amato, LauraGaregnani, María LorenaBlanco, EmilioCiencias EconómicasForecast poolingLarge datasetReal time forecastFactor ModelsWe pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future.Facultad de Ciencias Económicas2011-11info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/170412enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-99570-9-7info:eu-repo/semantics/altIdentifier/url/https://bd.aaep.org.ar/anales/works/works2011/Damato.pdfinfo:eu-repo/semantics/altIdentifier/issn/1852-0022info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:43:20Zoai:sedici.unlp.edu.ar:10915/170412Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:43:21.275SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Nowcasting economic activity in Argentina with many predictors |
title |
Nowcasting economic activity in Argentina with many predictors |
spellingShingle |
Nowcasting economic activity in Argentina with many predictors D'Amato, Laura Ciencias Económicas Forecast pooling Large dataset Real time forecast Factor Models |
title_short |
Nowcasting economic activity in Argentina with many predictors |
title_full |
Nowcasting economic activity in Argentina with many predictors |
title_fullStr |
Nowcasting economic activity in Argentina with many predictors |
title_full_unstemmed |
Nowcasting economic activity in Argentina with many predictors |
title_sort |
Nowcasting economic activity in Argentina with many predictors |
dc.creator.none.fl_str_mv |
D'Amato, Laura Garegnani, María Lorena Blanco, Emilio |
author |
D'Amato, Laura |
author_facet |
D'Amato, Laura Garegnani, María Lorena Blanco, Emilio |
author_role |
author |
author2 |
Garegnani, María Lorena Blanco, Emilio |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Económicas Forecast pooling Large dataset Real time forecast Factor Models |
topic |
Ciencias Económicas Forecast pooling Large dataset Real time forecast Factor Models |
dc.description.none.fl_txt_mv |
We pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future. Facultad de Ciencias Económicas |
description |
We pool a large data set of business cycle indicators to produce Nowcast of contemporaneous GDP growth. We also conduct Nowcast using factors for a restricted subset of the indicators. Using an AR(1) benchmark to compare the forecasting performance of both Nowcasts, we conclude that only the Nowcast with pooling outperforms this univariate model. The Giacomini and White (2004) test is employed to evaluate the out of sample forecasting performance of the pooling compared to the AR(1). In general, results indicate that a rich data set approach can provide valuable predictions about GDP behavior for the immediate future. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-11 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://sedici.unlp.edu.ar/handle/10915/170412 |
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http://sedici.unlp.edu.ar/handle/10915/170412 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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